A Procedure for Identification of Appropriate State Space and ARIMA Models Based on Time-Series Cross-Validation

Ramos, Patrícia; Oliveira, José Manuel

doi:10.3390/a9040076

Cited by 23 publications

(16 citation statements)

References 24 publications

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“…They range from simple techniques such as simple moving averages or exponential smoothing to Fourier analysis or ARIMA models. These methods should only be used to forecast demand at higher aggregation levels or to forecast products with no promotional intensity (Ramos et al, 2015;Ramos and Oliveira, 2016).…”

Section: Retail Sales Forecasting 84mentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

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Forecasting has always been in the forefront of decision making and planning. The uncertainty that surrounds the future is both exciting and challenging, with individuals and organisations seeking to minimise risks and maximise utilities. The lack of a free-lunch theorem implies the need for a diverse set of forecasting methods to tackle an array of applications. This unique article provides a non-systematic review of the theory and the practice of forecasting. We offer a wide range of theoretical, state-of-the-art models, methods, principles, and approaches to prepare, produce, organise, and evaluate forecasts. We then demonstrate how such theoretical concepts are applied in a variety of real-life contexts, including operations, economics, finance, energy, environment, and social good. We do not claim that this review is an exhaustive list of methods and applications. The list was compiled based on the expertise and interests of the authors. However, we wish that our encyclopedic presentation will offer a point of reference for the rich work that has been undertaken over the last decades, with some key insights for the future of the forecasting theory and practice.

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Section: Retail Sales Forecasting 84mentioning

confidence: 99%

Forecasting: theory and practice

Petropoulos,

Apiletti,

Assimakopoulos

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

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“…ARIMA models are generally accepted as one of the most versatile classes of models for forecasting time-series [ 21 , 22 ]. Many different types of stochastic seasonal and non-seasonal time-series can be represented by them.…”

Section: Pure Forecasting Modelsmentioning

confidence: 99%

“…ETS and ARIMA models are the two most widely-used approaches to time-series forecasting. They are based on different perspectives to the problem and often, but not always, perform differently, although they share some mathematically equivalent models [ 21 , 22 , 38 , 39 , 40 ]. ARIMA can potentially capture higher-order time-series dynamics than ETS [ 34 ].…”

Section: Empirical Studymentioning

confidence: 99%

Assessing the Performance of Hierarchical Forecasting Methods on the Retail Sector

Oliveira

Ramos

2019

Entropy

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Retailers need demand forecasts at different levels of aggregation in order to support a variety of decisions along the supply chain. To ensure aligned decision-making across the hierarchy, it is essential that forecasts at the most disaggregated level add up to forecasts at the aggregate levels above. It is not clear if these aggregate forecasts should be generated independently or by using an hierarchical forecasting method that ensures coherent decision-making at the different levels but does not guarantee, at least, the same accuracy. To give guidelines on this issue, our empirical study investigates the relative performance of independent and reconciled forecasting approaches, using real data from a Portuguese retailer. We consider two alternative forecasting model families for generating the base forecasts; namely, state space models and ARIMA. Appropriate models from both families are chosen for each time-series by minimising the bias-corrected Akaike information criteria. The results show significant improvements in forecast accuracy, providing valuable information to support management decisions. It is clear that reconciled forecasts using the Minimum Trace Shrinkage estimator (MinT-Shrink) generally improve on the accuracy of the ARIMA base forecasts for all levels and for the complete hierarchy, across all forecast horizons. The accuracy gains generally increase with the horizon, varying between 1.7% and 3.7% for the complete hierarchy. It is also evident that the gains in forecast accuracy are more substantial at the higher levels of aggregation, which means that the information about the individual dynamics of the series, which was lost due to aggregation, is brought back again from the lower levels of aggregation to the higher levels by the reconciliation process, substantially improving the forecast accuracy over the base forecasts.

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“…Fan and Hyndman [26] forecasted demand for electricity in the Australian National Electricity Market, saying that there should be different metrics and criteria for adaption of this forecast. As for Ramos and Oliveira [27], they produced a cross-validation procedure to determine appropriate models: the autoregressive integrated moving average model and state space model. Based on their study, such cross-validation procedure has been used to support accurate forecasting and accuracy enhancement.…”

Section: Literature Reviewmentioning

confidence: 99%

A Forecasting Model for Economic Growth and CO2 Emission Based on Industry 4.0 Political Policy under the Government Power: Adapting a Second-Order Autoregressive-SEM

Sutthichaimethee

Chatchorfa

Suyaprom

2019

Journal of Open Innovation: Technology, Market, and Complexity

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This research aims to forecast future economic and environmental growth for the next 16 years (2020–2035) according to the government’s strategic framework by applying the second order autoregressive-structural equation model (second order autoregressive-SEM). The model is validated by various measures, fits with the best model standards, meets all criteria of the goodness of fit, and is absent from any issues of heteroskedasticity, multicollinearity, autocorrelation, and non-normality. The proposed model is very distinct from other alternatives in that it produces the optimal outcome. Its mean absolute percentage error (MAPE) is 1.02% while the root mean square error (RMSE) is 1.51%. A comparison of the above results is carried out to compare the same values from other models, namely the regression linear model (ML model), back propagation neural network (BP model), artificial neural natural model (ANN model), gray model, and the autoregressive integrated moving average model (ARIMA model). The second order autoregressive-SEM is a model that is appropriate for long-term forecasting (2020–2035), and accounts for the specifics of the Thai government strategy set under the Industry 4.0 policy framework. The results of the long-term analysis indicate that the current political policy will result in continuous economic growth, where the gross national product (GNP) growth rate will climb up to 6.45% per annum by 2035, while the environment is being negatively affected. The study predicts that CO2 emissions will rise up to 97.52 Mt CO2 Eq. (2035). The forecasting model also reflects that the economy factor has an adjustment ability to equilibrium stronger than that of the environment factor; further, it shows that the relationship between the factors is causal. In addition, the political policy , economy , and environment factors are found to have both direct and indirect effects. As to the results, this study illustrates that the Industry 4.0 policy is still inefficient, as the carbon dioxide emissions are projected to be higher than the threshold for environment hazards and disasters which set to the limit of 80 Mt CO2 Eq. by 2035. The effect of such policy will put the environment at risk, and the government must take immediate action to respond to this urgency. Thus, the second order autoregressive-SEM model remains a significant model embedded with the adjustment ability to equilibrium and the applicability for various contexts in different sectors. This introduced model is a vital tool for assisting the national government to create policy that is effective and sustainable, and lead to positive development of the nation. This second order autoregressive-SEM model can be used as a resource for the management of both public policy and private enterprise.

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A Procedure for Identification of Appropriate State Space and ARIMA Models Based on Time-Series Cross-Validation

Cited by 23 publications

References 24 publications

Forecasting: theory and practice

Forecasting: theory and practice

Assessing the Performance of Hierarchical Forecasting Methods on the Retail Sector

A Forecasting Model for Economic Growth and CO2 Emission Based on Industry 4.0 Political Policy under the Government Power: Adapting a Second-Order Autoregressive-SEM

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